This project aims to achieve a fundamental advance in our understanding of how neural populations process and represent information within sensory cortex. By combining pioneering recording technology with new analytical tools and theoretical frameworks, this research effort will provide the first glimpse at how large numbers of neurons interact within the cortex during the processing of dynamic natural scenes. Silicon polytrodes will be used to record simultaneously from populations of 100+ neurons in cortex. The activity of these populations will be characterized in terms of response precision, sparsity, correlation, and LFP coherence. In order to elucidate the causal factors that contribute to stimulus-evoked responses in the cortex, the joint activity and stimuli will be fit with predictive models that attempt to capture the stimulus-response relationships of large neuronal ensembles. Finally, we will attempt to account for these relationships by building functional models that achieve theoretically-motivated information processing objectives for perception and cognition. The project is highly interdisciplinary in nature, combining the expertise of neurophysiologists, theoreticians, and engineers to answer questions that are beyond the scope of any one discipline. Intellectual merit. The question of how the cortex processes and represents sensory information has been the subject of neurophysiological and neuroanatomical investigation for at least four decades. While much has been learned from these efforts, there remain many fundamental, unanswered questions regarding the dynamical properties of neurons and the information processing capabilities of this system. The usual approach of studying single-unit responses to simple stimuli is limited in that it assumes - either explicitly or implicitly - that the system can be understood one component at a time. In a non-linear dynamical system it is difficult to predict how effects observed in isolation will behave when combined. Thus, in order to properly characterize and understand the dynamics of cortical circuits, it is necessary to observe the joint activities of large numbers of simultaneously recorded neurons in response to complex, timevarying signals arising from dynamic natural scenes. This project represents the first-ever attempt to thoroughly examine the joint responses of large numbers of neurons in the cortex during natural vision. Combined with the computational modeling and theoretical developments that will incorporate findings originating from these studies, this project has the potential to fundamentally advance our understanding of how cortical circuits work. Broader impacts. This project will provide research training to two graduate students, one in neuroscience (UC Berkeley) and one in engineering (Georgia Tech), and these studies will constitute the bulk of their Ph.D. theses. Efforts will be made to recruit women and underrepresented minorities into these positions. The methods developed and the results obtained from this study will be incorporated into coursework at UC Berkeley, Georgia Institute of Technology and Montana State University, and data will be made available on the NSF-funded CRCNS datasharing facility. Advancing our understanding of neural circuit dynamics within the cortex could lead to the development of viable therapies for myriad neurological disorders, and it is crucial to the development of neural prostheses. Furthermore, the proposed work will strengthen our infrastructure for further studies of the cortex by pioneering new simultaneous recording techniques and making the data publicly available as part of a CRCNS data sharing project.